Background: External ventricular drain (EVD) placement is the most frequently performed neurosurgical procedure for management of various conditions including hydrocephalus, traumatic brain injury, and stroke. State-of-the-art computational pattern recognition techniques could improve the safety and accuracy of EVD placement. Placement of the Kocher's point EVD is the most common neurosurgical procedure which is often performed in urgent conditions.
Objective: To present the development of a novel computer algorithm identifying appropriate anatomy and autonomously plan EVD placement on clinical computed tomography (CT) scans.
Methods: The algorithm was tested on 2 data sets containing 5-mm slice noncontrast CT scans. The first contained images of 300 patients without significant intracranial pathology (normal), the second of 43 patients with significant acute intracranial hemorrhage. Automated planning was performed by custom 2-tiered heuristic with run-time template selection in combination with refinement using nonlinear image registration.
Results: Automated EVD planning was accurate in 297 of 300 normal and 41 of 43 patient cases. In the normal data set, mean distance between Kocher's point and the ipsilateral foramen of Monro was 63 ± 3.1 mm in women and 65 ± 6.5 mm in men (P = .0008). Trajectory angle with respect to the sagittal plane was 91 ± 6° in women and 90 ± 6° in men (obtuse posterior) (P = .15); to the coronal plane, 85 ± 6° and 86 ± 5° in women and men (P = .12), respectively (acute lateral).
Conclusion: A combination of linear and nonlinear image registration techniques accurately planned EVD trajectory in 99% of normal scans and 95% of scans with significant intracranial hemorrhage.